Every recruiter has a role that dragged on for months and a good person who slipped through. EVO works both ends: it challenges the role before you spend the first week searching, and it reads every candidate in depth instead of matching keywords.
Most claims are backed up, but the discovery impact is described without numbers, so it loses points here.
Progression with growing scope, not just growing titles.
Discovery mentioned without an impact metric, so the result cannot be confirmed.
This and the other screens on this page are EVO's real screens, rebuilt in code so they load fast and stay readable on a phone. The data shown is illustrative.
Search firm owner, independent headhunter, recruiter on the hook for the result. A bad recommendation costs reputation, not just time. And your LinkedIn account is your most valuable asset.
The request comes in, you suspect it won't close, but you have no way to prove it to the hiring manager. Four weeks later the pipeline is empty and the hard conversation happens anyway, only later.
Head of HR or talent acquisition in an operation where a bad hire really hurts. You don't need more résumés: you need the few right ones to show up.
And if your problem is screening a thousand applications per role, EVO is not the tool, and we'd rather say so now. Our measure is the opposite: of every ten suggestions, one you would hire.
Tool after tool promised more résumés, faster, in more places. The recruiter ended up with a bigger pile of the same problem: whoever wrote the right word shows up; whoever lived the right experience and wrote it differently does not.
Now AI has sped up both sides. Applications per recruiter are up 412% since 2023, because candidates automated too, and the industry answered by building better filters for the flood.
We went the other way. EVO goes after the people who never applied, and reads each one in depth instead of filtering.
The title matches, the words are all there. Rises to the top of the list and becomes one of the first people contacted.
"SPM title, but the profile describes roadmap execution, not discovery. Several leadership claims with no evidence."
Does not have the title being searched and describes his career in different vocabulary. Never even appears on the list.
"Built the discovery practice from scratch and grew two PMs. The job title is different; the work is exactly what the role asks for."
In both cases the filter is wrong, and wrong in opposite directions: it passes the person with the right title and none of the practice, and rejects the one with the practice under a different title. That's why our measure is one hireable person in every ten suggestions, not a thousand résumés per role.
The 412% figure: rise in applications per recruiter since 2023, published by Greenhouse in 2026.
The company is named evo, as in evolution. Selling infallibility would be incoherent. These are the three promises we can keep, and the three we refuse to make.
Every system trained on human decisions carries bias, ours included. The difference is that EVO cites the evidence behind each claim, shows which history it used to calibrate that assessment, and hands back a portrait of your own criteria, so you can look at them from the outside.
EVO will suggest people you reject. When that happens, it records the reason in structured vocabulary and adjusts the next analysis. And it goes further: it follows the people you hired at 90 days and at 12 months, to find out whether the recommendation was actually good.
No score eliminates a candidate automatically. Tests and behavioral profiles become interview material, never a cut. Human review is mandatory and it is recorded. You remain the one accountable for the hire.
Each decision you make teaches the system, and the system gives back what it learned as a better question. It isn't a tool you use: it's a second pair of eyes that gets better the more you work with it.
These aren't extra items on a feature list. They're the four architectural choices that hold the promise up, and that, in the research we did, no competitor had made.
Before the first search, it interviews whoever opened the role and challenges what it finds: an impossible requirement, a salary off the market, an incoherent seniority level, a demand that rules out good candidates for nothing. And it won't write the description while a blocker is still open.
You get the argument ready to take to the hiring manager: acknowledgment, data, proposal and consequence.
Of the responsibilities we captured, six are people leadership: defining what each analyst works on, running development plans and 1:1s, and owning the team’s capacity.
The practical consequence: you will attract a strong IC who turns down the management part, or a manager who accepts and then asks for the title back. Both scenarios cost you a cycle.
Reclassify as Team Leader and keep the scope. The range goes up ~25%: salary intel estimates R$ 18–24k for a Discovery TL in São Paulo, against R$ 14–19k for a senior specialist.
"I want your confirmation on both blockers before I write. I am not drafting this with the wrong seniority."
A desktop app runs on your machine and uses the Chrome you already opened and already signed into. No password handed over, no credential stored, no browsing coming out of a data center. The server never opens a connection to your computer: it's the app that asks for work when you're active.
Variable pauses, a business-hours window and a daily cap that you set. On a team, each search runs in the Chrome of whichever hunter you pick.
Every rejection and every shortlist captures the reason in structured vocabulary, and that feeds the next analysis. Deeper still: the real outcome comes back. Did you hire? How was that person at 90 days? And at 12 months?
And the screen shows exactly which history went into each assessment. Not a black box: open books.
"Of your shortlists based on trajectory, 71% became hires that lasted past 90 days: the pattern that looked like bias is delivering. Shortlists based on evidence converted at only 45%."
Base: 17 closed processes with a recorded outcome · N is still small, read it as a trend.The bias mirror compares what you prioritize with what EVO prioritizes and returns the gap in plain text. It is not an accusation and not a prompt to act: it is a reading. It may well be a deliberate strategy of yours, and EVO says so.
Then it sets each of your patterns against the real outcome of the hires. You find out not just what you do differently, but whether it is working.
"You shortlist on trajectory about 3× more than on evidence: EVO tends to balance those two axes. It may be strategy (hunting for talent on a fast climb) or a pattern worth watching."
ⓘ A reading, with nothing to click. EVO carries this context into calibrating the next assessment, and sets each of your patterns against the real outcome of the hires.
Almost every recruiting product treats the job description as a text field to fill in. In EVO it's a six-phase conversation, and what comes out of it feeds the search, the screening, the interview and the test.
You describe the need in plain language
Eight dimensions of discovery become recorded facts
What doesn't add up gets contested, with an argument for the manager
Thirteen sections anchored in facts, never in guesswork
A second agent reviews the text without seeing the conversation
Becomes the input for everything that follows
During the interview EVO writes nothing, on purpose: polished text written too soon becomes an anchor, and you end up defending the sentence instead of the role. Drafting only opens once the blockers are resolved.
See EVO JD from the inside →Every stage exists to prove the same point: read the person in depth instead of filtering, with the decision always on your side.
EVO interviews whoever opened the role, challenges what doesn't add up, and only then writes.
A search strategy with target companies and adjacent titles, running on your own account.
A deep read of each profile, with cited evidence and offer-acceptance odds.
A message that cites something real from their career, in your voice, with your approval.
An interview journey, a guide with a rubric and a test built for that role.
It reaches out like a person. The message cites something real from that person's career, not a template with the name swapped. And it only goes out after you approve it.
Below what the stage expects: becomes an interview probe, not a cut.
"Walk me through the most recent A/B test you designed: the hypothesis, the sample size, and what you decided when the result came back ambiguous."
Adjustments are audited. The score is informative: it never eliminates anyone on its own.
Proctoring asks whether someone else is in the room. We ask whether you did the work. The test is generated for that role, and in a controlled experiment with original questions, the people cheating with AI scored lower than the ones answering honestly. The defense is the question a camera never asks.
Fifteen modules, organized in the order you use them. Each one has its own page with the screens from the inside.
Interviews whoever opened the role, challenges what doesn't add up and only then writes, in 13 sections, with 14 problem detectors.
See inside →AlignmentWhoever opened the role answers the agenda through a link, with no account to create. The answer becomes a confirmed fact and reprocesses the JD.
See inside →RealismThe salary asked against the market, funnel reach per requirement and the risks being taken, versioned at every negotiation.
See inside →Target companies, adjacent titles and career signals, all editable. Live sweep on your own account, at your own pace.
See inside →Local agentAn app on your machine uses the session you already signed into. No password, no cloud, with a cap that you set.
See inside →Your own databaseEvery candidate you have seen stays in a database of yours. Wishlist and blacklist flags follow the person across any role.
See inside →A score across four dimensions with text for each one, a read of the career, strengths, concerns and gaps, plus the odds of acceptance.
See inside →SalariesA range estimated by company, title and seniority, with a stated confidence level. Feeds the acceptance prediction.
See inside →TestsA technical test generated from the role, languages with spoken assessment at CEFR level, and behavioral, with a live defense of the work.
See inside →Invitation, message or InMail in your voice, citing something real from the career, sent from your account, after your approval.
See inside →InterviewsStages on a canvas with parallel groups and transitions by outcome. External interviewers score through a portal of their own.
See inside →GuideA guide per stage with a rubric of anchors, signals to look for and red flags, plus probes built from that candidate’s gaps.
See inside →Rejection and shortlist reasons, distribution by dimension and the real outcome at 90 days and 12 months feeding back into the analyses.
See inside →BiasCompares what you prioritize with what EVO prioritizes and returns the gap in plain text, set against the real outcome.
See inside →TeamSeveral hunters, roles with permission per resource, teams and clients. Two-level auditing and a manual on every screen.
See inside →It is every recruiter's first question, and the product's architecture was designed around it.
The app uses the Chrome session you already opened and signed into. No LinkedIn credential is typed, transmitted or stored anywhere.
All the browsing happens on your computer. Our server never opens a connection to your machine: it's the app that asks for work when you're active.
Variable pauses, business hours and a daily cap on profiles that you define. No bursts of access that set off alarms on the platform.
Each company operates in an isolated organization, with separation enforced at the data layer and verified automatically on every change to the system.
Test results and behavioral profiles become interview material, never an automatic cut. Human review is mandatory and recorded, as Brazil's data protection law requires.
Surveillance detects the environment, not authorship. An impostor with the camera on gets through. That is why the candidate defends their own work live: it is what ties the person to what they delivered.
On every screen there is a button to report a problem or suggest an improvement. You write two lines and that's it. The system attaches which screen you were on and what you were doing, so you don't have to retrace the steps or take a screenshot.
You get an email confirmation right away. A confirmed bug gets fixed, and we tell you when the fix ships. An approved suggestion becomes an improvement, and the screen's manual is updated along with it.
A button anywhere in the system, without leaving what you were doing
Screen, action and moment travel attached, with nothing for you to explain
You know it arrived, instead of waiting in the dark
No bureaucratic triage, no ticket number, no approval queue
And the screen's manual is updated along with the change
There is no sales demo in the way. You sign in, build the role and watch EVO work.
Sign in with Google or get a link by email, no password at all. Give your organization a name and you are in. No long form, no waiting for approval.
2 minutesEVO interviews you about it, challenges what doesn't add up and writes the description. This runs without installing anything.
20 to 40 minutes of conversationThe app sits in your system tray and connects to the Chrome you already use. Pair it with a code and the LinkedIn search begins.
5 minutes to installThe risk is never zero with any tool that touches LinkedIn, and anyone who says otherwise is selling. What we do: no automated login, no server reaching into your account, a human pace with variable pauses, business hours and a daily cap that you set. Every operation was tested on a real account before it became a feature.
Yes, a light app that sits in the system tray (Windows or Mac) and talks to the Chrome you already use. It is what makes it possible to work through your own account instead of asking for your password. Installation takes a few minutes and pairing is by code.
Only if you want it to. The default is approval mode: EVO writes, you read, edit if you like and release. Anyone who prefers can switch on automatic mode once they trust the voice.
EVO doesn't compete with your ATS. It solves what no ATS solves. Greenhouse, Ashby and Lever are systems of record for people who applied: none of them goes looking for the people who never did. That is exactly where EVO works. Today it runs the process end to end inside itself, with open export of roles and data; direct integration with market ATSs does not exist yet, and if that is a requirement for you, it is worth talking first.
Because they explain their own work, live, answering questions generated from what they themselves wrote, with no going back. Surveillance answers a different question: whether someone else is in the room. It does not answer who did the work. Cases documented in 2025 show impostors passing technical interviews with the camera on and real technical skill. What the camera misses, defending the solution catches.
No automated decision eliminates a candidate on its own. Human review is mandatory and recorded, as article 20 of Brazil's LGPD requires. Every access to sensitive data is audited, and each organization sees only its own data.
No, and that is deliberate. Our measure is precision: of every ten suggestions, one you would hire. If your problem is processing volume, there are better tools for it, and we would rather say so up front.
You sign in with Google or an email link, add the role, and EVO starts by interviewing you about it. The LinkedIn search comes later, once you install the app.
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